PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonparametric sequential prediction of time series
K Bleakley, G Biau, Laszlo Gyorfi and Gyorgy Ottucsak
Journal of Nonparametric Statistics 2009. ISSN Print: 1048-5252 Online: 1029-0311

Abstract

Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of 'experts' and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalized cumulative prediction error.

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EPrint Type:Article
Additional Information:http://arxiv.org/abs/0801.0327
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4987
Deposited By:Laszlo Gyorfi
Deposited On:18 March 2009